Novak et al. [1]} define two complexity measures from the perspective of model sensitivity, and identify an empirical correlation between the complexity measures and model generalization capability. They show that operations that lead to poor generalization, such as full batch training, correspond to high sensitivity, and in turn imply high effective complexity. Similarly, operations that lead to good generalization, such as data augmentation, correspond to low sensitivity, and thus imply low effective complexity.
